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Didn't the same argument apply in general to why NNs weren't particularly useful for anything ~30 years ago? Using a network of tensors to compute intelligence
by SomeStupidPoint 9y ago
Didn't the same argument apply in general to why NNs weren't particularly useful for anything ~30 years ago?
Using a network of tensors to compute intelligence is incredibly old (I believe, dating back about 80 years), but has only recently become tractable to do for any complex tasks.
However, in the past ~30 years, we've gone from "intractable for moderate problems" to "world champion at go", "able to detect cancer in images as well as experts", etc. My contention is that in another ~30 years, we'll see a step sufficient for "can do average at most intellectual activities", even if that's just having the storage to keep 10,000 task specific NNs (of AlphaGo sophistication) on hand to interpolate all actions as mixes of specialist tasks. Do you really not think there's a strong heuristic case for that? (I would contend that you should be able to point to a specific task you don't think it will be able to do on that timeline -- do you know of such a task?)
The proof was merely that we're not barking up a theoretically dead tree -- we have to rely on heuristics for if it will eventually converge to tractable.
- leereeves 9y agoI'm not arguing that NNs aren't capable of AGI, merely that the ability to compute the standard model leaves the far more difficult question of whether the problem is tractable, as you said. The standard model could be computed directly without NNs, which I think we agree wouldn't be a useful way to approach AGI.
- mannykannot 9y ago> Didn't the same argument apply in general to why NNs weren't particularly useful for anything ~30 years ago? Not unless there was good reason to expect that every other possible application of NNs would be as difficult to achieve as general intelligence.